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Thomas Zickler CERN Normal - conducting accelerator magnets Lecture 4: Applied numerical design Joint Universities Accelerator School JUAS 2020 Archamps, France, 2. – 4. March 2020

Normal-conducting accelerator magnets Lecture 4: Applied … · 2020. 4. 1. · – for coil and yoke material – required for transient eddy current calculations Mechanical and

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  • Thomas Zickler

    CERN

    Normal-conducting accelerator magnetsLecture 4: Applied numerical design

    Joint Universities Accelerator School

    JUAS 2020

    Archamps, France, 2. – 4. March 2020

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    Lecture 4: Numerical design

    Which code shall I use?

    Introduction to 2D numerical design

    How to evaluate the results

    Field analysis

    Typical application examples

    2

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    Numerical designCommon computer codes: Opera (2D) or Tosca (3D), Poisson, ANSYS, Roxie, Magnus, Magnet, Mermaid, Radia, FEMM, COMSOL, etc…

    Technique is iterative

    – calculate field generated by a defined geometry

    – adjust geometry until desired distribution is achieved

    Computing time increases for high accuracy solutions, non-linear problems and time dependent analysis compromise between accuracy and computing time

    FEM codes are powerful tools, but be cautious:

    – Always check results if they are ‘physical reasonable’

    – Use FEM for quantifying, not to qualify3

    2D

    •2D analysis is often sufficient

    •magnetic solvers allow currents only perpendicular to the plane

    • fast

    3D

    •produces large amount of elements

    •mesh generation and computation takes significantly longer

    •end effects included

    •powerful modeller

    2D-numerical simulation – Design evaluation – Field analysis – Examples – Summary

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    Numerical design process

    Design process in 2D (similar in 3D):

    Create the model (pre-processor or modeller)

    Define boundary conditions, set material properties

    Calculations (solver)

    Visualize and asses the results (post-processor)

    Optimization by adjusting the geometry (manually or optimization code)

    2D-numerical simulation – Design evaluation – Field analysis – Examples – Summary

    4

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    Creating the model

    GUI vs. Script

    5

    2D-numerical simulation – Design evaluation – Field analysis – Examples – Summary

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    Model symmetries

    Note: one eighth of quadrupole could be used with opposite symmetries defined on horizontal and y = x axis

    6

    2D-numerical simulation – Design evaluation – Field analysis – Examples – Summary

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    Boundary conditions

    7

    2D-numerical simulation – Design evaluation – Field analysis – Examples – Summary

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    Material properties

    Permeability:

    – either fixed for linear solution

    – or permeability curve for non-linear solution

    – can be anisotropic

    – apply correction for steel packing factor

    – pre-defined curves available

    Conductivity:

    – for coil and yoke material

    – required for transient eddy current calculations

    Mechanical and thermal properties:

    – in case of combined structural or thermal analysis

    Current density in the coilsData source: Thyssen/Germany

    8

    2D-numerical simulation – Design evaluation – Field analysis – Examples – Summary

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    Mesh generation

    element shape element type element size

    9

    2D-numerical simulation – Design evaluation – Field analysis – Examples – Summary

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    Data processing

    • linear: predefined constant permeability for a single calculation

    • non-linear: permeability table for iterative calculations

    Solution

    • static

    • steady state (sine function)

    • transient (ramp, step, arbitrary function, ...)Solver types

    • number of iterations,

    • convergence criteria

    • precision to be achieved, etc...Solver settings

    10

    2D-numerical simulation – Design evaluation – Field analysis – Examples – Summary

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    Analyzing the results

    With the help of the post-processor, field distribution and field quality and be visualized in various forms on the pre-processor model:

    – Field lines and colour contours plots of flux, field, and current density

    – Graphs showing absolute or relative field distribution

    – Homogeneity plots

    11

    2D-numerical simulation – Design evaluation – Field analysis – Examples – Summary

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    Field homogeneity in a dipole

    Homogeneity along the x-axis

    Homogeneity along GFR boundaryA simple judgment of the field quality can be done by plotting the field homogeneity

    1)0,0(

    ),(

    0

    y

    y

    B

    yxB

    B

    B

    Homogeneity along the x-axis

    Homogeneity along GFR boundary

    %01.00

    B

    B

    12

    2D-numerical simulation – Design evaluation – Field analysis – Examples – Summary

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    Field homogeneity in a dipole

    13

    2D-numerical simulation – Design evaluation – Field analysis – Examples – Summary

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    Saturation and field quality

    Field quality can vary with field strength due to saturation

    Also very low fields can disturb the field quality significantly

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    2D-numerical simulation – Design evaluation – Field analysis – Examples – Summary

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    Field quality in a quadrupole

    -1.0E-03

    -7.5E-04

    -5.0E-04

    -2.5E-04

    0.0E+00

    2.5E-04

    5.0E-04

    7.5E-04

    1.0E-03

    -17.5 -12.5 -7.5 -2.5 2.5 7.5 12.5 17.5

    Gra

    die

    nt err

    or

    x [mm]

    Gradient error along the x-axis

    CERN proposal V2a P=0.98% R=15 mm I=8.2031 A; tapered poles (OPERA)BINP proposal V2b (18.7.) R=15 mm I=8.2031 A (MERMAID)

    -1.0E-04

    -7.5E-05

    -5.0E-05

    -2.5E-05

    0.0E+00

    2.5E-05

    5.0E-05

    7.5E-05

    1.0E-04

    0.0 20.0 40.0 60.0 80.0 100.0 120.0 140.0 160.0 180.0

    Gra

    die

    nt

    err

    or

    angle [degree]

    Gradient error along the GFR boundary

    CERN proposal V2a P=0.98% R=15 mm I=8.2031 A; tapered poles (OPERA)

    BINP proposal V2b (18.7.) R=15 mm I=8.2031 A (MERMAID)Gradient homogeneity along the x-axis

    Gradient homogeneity along circular GFR

    1)0,0('

    ),(

    22

    yxB

    yxB

    %1.0'

    '

    0

    B

    B

    Field error in a quadrupole

    1)0,0('

    ),('

    '

    '

    0

    B

    yxB

    B

    B

    15

    2D-numerical simulation – Design evaluation – Field analysis – Examples – Summary

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    Saturation and field quality

    Field quality varies with field strength due to saturation

    16

    2D-numerical simulation – Design evaluation – Field analysis – Examples – Summary

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    Saturation

    0

    1

    2

    3

    4

    5

    6

    7

    8

    9

    10

    0

    0.5

    1

    1.5

    2

    2.5

    0 500 1000 1500 2000 2500 3000

    Sa

    tura

    tio

    n [

    %]

    Flu

    x d

    en

    sit

    y [

    B]

    Current [A]

    non-linear

    linear

    Saturation

    17

    2D-numerical simulation – Design evaluation – Field analysis – Examples – Summary

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    Field analysis

    Picking up from lecture 1

    and introducing dimensionless normalized multipole coefficients

    𝑏𝑛 =𝐵n

    𝐵N104 and 𝑎𝑛 =

    𝐴𝑛

    𝐵N104

    with BN being the fundamental field of a magnet: BN (dipole)=B1; BN (quad)=B2; …

    we can describe each magnet by its ideal fundamental field and higher order harmonic distortions:

    1

    41

    ( ) ( )10

    n

    Ny x n n

    n ref

    B zB z iB z b ia

    R

    18

    Harmonic distortionsFundamental field

    𝐵𝑦 𝑧 + 𝑖𝐵𝑥 𝑧 =

    𝑛=1

    𝐵𝑛 + 𝑖𝐴𝑛𝑧

    𝑅𝑟𝑒𝑓

    𝑛−1

    2D-numerical simulation – Design evaluation – Field analysis – Examples – Summary

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    Field analysis

    The field quality of a magnet can be also described by:

    • Homogeneity plot: – difference between the actual field B and the ideal field Bid, normalized by the ideal field Bid

    – can be expressed by multipole coefficients: for a dipole with

    • Harmonic distortion factor Fd :

    Note: For good field quality, Fd should be a few units in 10-4

    Δ𝐵

    𝐵=

    𝐵 𝑥, 𝑦 − 𝐵𝑖𝑑 𝑥, 𝑦

    𝐵𝑖𝑑 𝑥, 𝑦

    2 2

    Re Re

    1;

    ( ) ( ) ( )K

    d f n ref n f

    n n N

    F R b R a R

    𝐵𝑦,𝑖𝑑 𝑥 = 𝐵1

    𝐵𝑦 𝑥 = 𝐵1 +𝐵1

    104𝑏2

    𝑥

    𝑅𝑟𝑒𝑓+ 𝑏3

    𝑥

    𝑅𝑟𝑒𝑓

    2

    + 𝑏4𝑥

    𝑅𝑟𝑒𝑓

    3

    + ⋯

    Δ𝐵

    𝐵(𝑥) =

    1

    104𝑏2

    𝑥

    𝑅𝑟𝑒𝑓+ 𝑏3

    𝑥

    𝑅𝑟𝑒𝑓

    2

    + 𝑏4𝑥

    𝑅𝑟𝑒𝑓

    3

    + ⋯

    19

    2D-numerical simulation – Design evaluation – Field analysis – Examples – Summary

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    Field analysis

    Multipole errors can be divided into two families:

    ‚Allowed‘ multipoles are design intrinsic and result from the finite size of the poles

    n: order of multipole component

    N: order of the fundamental field

    m: integer number (m≥1)

    fully symmetric dipole

    allowed: b3, b5, b7, b9, etc.

    non-allowed: all others

    fully symmetric quadrupole

    allowed: b6, b10, b14, b18, etc.

    non-allowed: all others

    fully symmetric sextupole

    allowed: b9, b15, b21, etc.

    non-allowed: all others

    ‚Non-allowed‘ multipoles result from a violation of symmetry and indicate a fabrication or assembly error

    )12( mNn

    20

    2D-numerical simulation – Design evaluation – Field analysis – Examples – Summary

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    Asymmetries

    Asymmetries generating ‘non-allowed’ harmonics

    n = 2, 4, 6, ... n = 3, 6, 9, ...

    n = 4 (neg.) n = 4 (pos.) n = 3 n = 2, 3

    These errors can seriously affect machine behaviour and must be controlled!

    Comprehensive studies about the influence of manufacturing errors on the field quality have been done by K. Halbach.

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    2D-numerical simulation – Design evaluation – Field analysis – Examples – Summary

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    Asymmetry in a C-magnet

    • C-magnet: one-fold symmetry

    • Since the contribution to the integral in the iron has different path lengths

    • Finite (low) permeability will create lower B on the outside of the gap than on the inside

    • Generates ‘forbidden’ harmonics with n = 2, 4, 6, ... changing with saturation

    • Quadrupole term resulting in a gradient around 0.1% across the pole

    .constdlHNI

    22

    2D-numerical simulation – Design evaluation – Field analysis – Examples – Summary

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    ‚Shimming‘ (often done by ‘try-and-error’) can improve the field homogeneity

    1. Add material on the pole edges: field will rise and then fall2. Remove some material: curve will flatten3. Round off corners: takes away saturation peak on edges4. Pole tapering: reduces pole root saturation -> Rogowsky profile

    Pole optimization

    -0.02

    -0.015

    -0.01

    -0.005

    0

    0.005

    0.01

    0.015

    0.02

    ΔB

    /B

    -0.02

    -0.015

    -0.01

    -0.005

    0

    0.005

    0.01

    0.015

    0.02

    ΔB

    /B

    -0.02

    -0.015

    -0.01

    -0.005

    0

    0.005

    0.01

    0.015

    0.02

    ΔB

    /B

    -0.02

    -0.015

    -0.01

    -0.005

    0

    0.005

    0.01

    0.015

    0.02

    ΔB

    /B

    -0.02

    -0.015

    -0.01

    -0.005

    0

    0.005

    0.01

    0.015

    0.02

    ΔB

    /B

    -0.02

    -0.015

    -0.01

    -0.005

    0

    0.005

    0.01

    0.015

    0.02

    ΔB

    /B

    Case 1

    Case 2

    Case 3

    Case 4

    Case 5

    23

    2D-numerical simulation – Design evaluation – Field analysis – Examples – Summary

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    Rogowsky roll-off

    The ‘Rogowsky’ profile provides the maximum rate of increase in gap with a monotonic decrease in flux density at the surface, i.e. no saturation at the pole edges!

    The edge profile is shaped according to:

    For an optimized pole:

    x: pole overhang normalized to the gap a: pole overhang: excess pole beyond the edge of the good field region to reach the required

    field uniformity

    h: magnet gap

    1exp

    2 h

    xhhy

    10-1-2

    0

    1

    2

    25.0ln14.020

    B

    B

    h

    axoptimized

    h

    aGFR

    24

    2D-numerical simulation – Design evaluation – Field analysis – Examples – Summary

    x

    y

    h/2

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    3D Design

    Becomes necessary to study:– the longitudinal field distribution

    – end effects in the yoke

    – end effects from coils

    – magnets where the aperture is large compared to the length

    – spacial field distribution

    – particle motion in electro-magnetic fields

    10 Gauss iso-potential surface

    Interference study

    3D-field homogeneity25

    2D-numerical simulation – Design evaluation – Field analysis – Examples – Summary

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    Case 1: A material problem

    Welding seam on stainless-steel vacuum chamber:

    • GFR radius: 30 mm

    • Chamber radius: 35 mm

    • Welding seam diameter: 1 mm

    • Rel. permeability of 316 LN: < 1.001

    A small distortion can significantly influence the field quality in the GFR!

    μr=1.001 μr=1.1

    μr=1.01

    26

    2D-numerical simulation – Design evaluation – Field analysis – Examples – Summary

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    Case 2: An interference problem

    Significant attenuation of the corrector field due to the close presence of two quadrupole yokes

    Courtesy of A. Vorozhtsov

    27

    2D-numerical simulation – Design evaluation – Field analysis – Examples – Summary

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    Case 3: Mechanical deformation

    • Mechanical deformation due to magnetic pressure can influence the field homogeneity

    • Multi-physics models can help to quantify the effect

    Courtesy of D. Schoerling

    Deformation

    von Mises stress

    Field homogeneity calculated for the center line of the magnet with ANSYS magnetic, ANSYS structural + magnetic, and Opera ST 2D

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    2D-numerical simulation – Design evaluation – Field analysis – Examples – Summary

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    Limitations of numerial calculation

    Advantages• predict behaviour without having the physical object• for relatively simple cases they are fast and inexpensive

    Limitations• multi-physics model: including all couplings (thermal, mechanical) and phenomena

    (magnetostriction, magneto-resistivity …) that may be relevant is very complex and expensive

    • off-nominal geometry: random assembly errors can dominate field distribution and quality; often, a large number of degrees-of-freedom and the resulting combinatorial explosion makes Monte Carlo prediction costly

    • material properties uncertainty : inhomogeneous properties cannot practically be measured throughout volume; even homogeneous materials can be measured only within 2-5% typical accuracy

    • numerical errors: e.g. singularities in re-entrant corners, boundary location of open regions may spoil results; special techniques (special corner elements, BEM) require special skills and time

    • high cost of detailed 3D models ( x2~3 ); transient simulations increase computing time significantly

    Computer simulation targeting

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    Summary

    • A large varity of FE-codes with different features exist – the right choice depends of the complexity of the problem

    • The FE-models shall be as simple as possible and adapted to the problem to reduce computing time

    • Numeric computations should be used to quantify, not to qualify

    • Benchmarking the results with measurements is a good practice

    • Computer simulations have a lot of advantages, but also their limitations

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    2D-numerical simulation – Design evaluation – Field analysis – Examples – Summary